There are several drivers to data visualisation changes. The volume of data held by organisations and freely available from many online sources is increasing exponentially. The type of data is also increasing and changing. A lot of businesses use and analyse unstructured data like customer feedback, users online interaction indicators, and emotions contained in tweets and social media messages.
Improvements in technology is also impacting on the type of data being collected and how it can be stored and processed at a phenomenal speed.
These drivers are very closely linked to the three major trends dominating data visualisation.
According to Daniel Yuen from Gartner:
“Mobile use may now be the most significant consumer technology when it comes to improving BI adoption. Although mobility enables BI to attract users and reach new constituencies in an organisation, ease of use and an engaging experience are the critical success factors for determining implementation success.”
Users will increasingly use their smart phones, tablets and other mobile devices to access the data visualisation displays. It means that the charts you design will need to be flexible enough to not only fit into different size screens with different resolutions but should be scalable to allow a clear view.
According to the Pitney Bowes white paper on Location Intelligence: The New Geography of Business, more than 80% of all data maintained by an organisation has a location component. This data is collected all the time, feeding a never-ending stream of information from sensors, mobile phones and other location based devices as well as more static data which quite often is location sensitive.
For this reason, the ability to handle maps, drill down into information based on a specific region, or use data that is automatically refreshed according to a specific location is becoming more and more important and can lead to a richer data visualisation experience for the user.
The wide range and free availability of so called big data coming from disparate sources, which can be integrated with the internal organisational data sets, can be a dream scenario for data analysts and decision-makers.
Visualisations based on data mashups offer much richer and context embedded charts as they can, for example, pull together data from governmental organisations, Office of National Statistics, Google analytics and internal organisational data to name a few.
While some of the higher education practitioners might argue that this trend is not as applicable to the sector, which tends to deal with much smaller datasets, this deluge of data availability present great opportunities in the era of education globalisation and expansion of international networks.